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Model distance normalization (D-Norm) is one of the useful score normalization approaches in automatic speaker verification (ASV) systems. The main advantage of D-Norm lies in that it does not need any additional speech data or external speaker population, as opposed to the other state-of-the-art score normalization approaches. But still, it has some drawbacks, e.g., the Monte-Carlo based Kullback-Leibler...
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